Lesson deck
All RAG types flashcards
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- Front
- When does HyDE help and what is its risk?
- Back
- Helps when query and documents are phrased very differently. Risk: the hypothetical answer can be wrong and bias retrieval toward it.
- Front
- Self-RAG vs CRAG vs Adaptive RAG?
- Back
- Self-RAG: a trained model emits reflection signals and critiques itself. CRAG: grade retrieved docs, fall back (rewrite or web) when weak. Adaptive: route each query to no retrieval, single-shot or multi-step.
- Front
- What is the decision rule for choosing a RAG variant?
- Back
- Start with the simplest design that meets the quality bar, measure, and add complexity only for a failure you can name. Each extra call adds latency, cost and a failure mode.
- Front
- Naive, advanced, modular, agentic: one line each.
- Back
- Naive: retrieve once, generate once. Advanced: adds rewrite, rerank, filters. Modular: swappable stages wired by a router. Agentic: the LLM decides whether, what and how often to retrieve.
- Front
- When do you reach for GraphRAG, and what does it cost?
- Back
- For multi-hop or corpus-wide questions that top-k similarity handles poorly. Cost: expensive indexing, a graph that can go stale, and extraction errors that propagate.
- Front
- Name two things agentic RAG needs to be safe in production.
- Back
- Step limits to prevent loops, and tracing plus guardrails to evaluate and secure the many tool calls. Latency and cost become unpredictable without them.